AI is evolving at a speed that’s leaving many large organizations struggling to keep pace. Recent surveys show widespread experimentation with AI across industries, but the reality is that over 88% of AI pilots never make it to production.
For IT leaders, the pattern is all too familiar: a compelling startup demo kicks off a pilot full of promise, but months later, little has changed. The pilot drags on, valuable time and resources are spent, and yet nothing makes it past the test phase. Meanwhile, the competitive landscape shifts, AI models evolve, and internal confidence in scaling AI begins to erode. So, what’s going wrong?
For the past decade, we’ve helped corporates build meaningful relationships with startups. When the AI wave began, we noticed a familiar pattern. Companies rushed to explore generative and predictive tools, launching proof-of-concepts that too often remained siloed, unvalidated, and eventually abandoned. There are also many instances in which too many use cases are explored at once, or various stakeholders get involved, leading to a stalemate on which tool to adopt, especially if some use cases underperform or another tool is preferred for a specific application.
Along the way, we’ve identified a few core reasons so many pilots fall short and what successful ones do differently.
Most AI Pilots Are Set Up to Stall
The biggest misconception we hear is: “We already know how to run pilots. Our challenge is scaling.” But how you run the pilot is the key to scale. Traditional pilot models treat scaling as something that comes after success is proven. In reality, the foundations for scale, such as change management, stakeholder alignment, and cross-functional engagement, must be built during the pilot itself.
Without this, even technically successful proofs of concept struggle to gain traction. The IT team may be on board, but if legal hasn’t been involved, compliance becomes a blocker. If end users aren’t engaged early, adoption lags. And if success metrics aren’t aligned to business outcomes, no one knows what “good” looks like.
The Real Bottleneck Is Trust, Not Tech
It’s easy to assume that AI’s biggest hurdles are algorithmic. But more often than not, the biggest friction points are cultural. Even the most accurate AI solution will face resistance if its outputs aren’t trusted or understood. In heavily regulated industries like financial services or healthcare, internal teams often hesitate to move forward without full transparency on data lineage, model behavior, and bias mitigation.
We’re seeing multiple AI startups pivot for this very reason. One leading retailer partnered with an innovative synthetic audiences startup that delivered exactly what the retailer’s marketing leaders asked for, but the marketing team ultimately didn’t trust the insights because the product didn’t align with their existing workflows for audience testing. Despite the model’s performance, uncertainty around how to interpret or validate the results stalled adoption. The startup has since repositioned around a broader trend prediction offering, entering a more crowded but better-understood market.
To navigate these internal barriers, many AI startups are now layering services on top of their SaaS products, offering hands-on implementation support, workflow alignment, and training. It’s a way to clear the path ahead of known roadblocks and accelerate adoption in environments where trust, clarity, and internal alignment matter as much as technical performance.
Speed Now Beats Size
The traditional enterprise pilot playbook was designed for slower technology cycles such as ERP implementations and multi-year cloud migrations. AI is different. Models evolve in weeks. This volatility is exactly why corporates need faster, more agile pilot frameworks. For our members, we’ve introduced a rapid prototyping stage designed to “fail fast,” helping teams test assumptions, refine problem statements, and evaluate ROI before committing major resources. It’s a way to experiment with guardrails, reducing risk while still moving fast enough to keep pace with innovation.
And that matters. The organizations that succeed with AI won’t be the ones spending the most. They’ll be the ones that learn the fastest.
AI Success Is a Team Sport
One of the most surprising lessons we’ve learned is that the success of an AI pilot depends less on the technology and more on the people driving it. We recently worked with a financial services client in the Middle East who was eager to explore AI but felt overwhelmed by the sheer number of options. More than 20 startups were in play, multiple departments were competing for attention, and there was no clear framework for making decisions. Over six months, we helped them prioritize, pilot, and implement real solutions in credit scoring, personalization, and internal training, compressing an 18-month roadmap into one quarter.
The reason it worked? The client didn’t just “run pilots.” They built an internal operating rhythm. They had stakeholder champions across functions, aligned on KPIs early, and created internal feedback loops that ensured learnings from one pilot accelerated the next.
Don’t Use the Old Playbook
If there’s one takeaway for IT executives navigating AI adoption, it’s to avoid applying a traditional software procurement mindset to AI. This isn’t about static RFPs and linear timelines. AI adoption is iterative. The problem you start with may not be the one you end up solving. That’s not a flaw. It’s the process working. The best corporate leaders we work with embrace this ambiguity, provided there are clear decision points and governance frameworks along the way.
Scaling AI isn’t about luck or hoping a single pilot succeeds. It requires a deliberate system that reduces risk, strengthens internal capabilities, and delivers real business results. As enterprises move to turn AI’s promise into performance, shifting from stalled pilots to confident production will be the key to lasting impact.
